skills.rest
tidymodelstidymodelsOfficial·3 Agent Skills Included

yardstick

Model performance metrics, evaluation, and R package maintenance

Measures how well machine learning models perform using tidy data principles in R. Calculates accuracy, ROC AUC, precision, recall, and fairness metrics across binary, multiclass, and survival models. Eliminates manual metric coding and inconsistent evaluation across resamples. Includes guided workflows for detecting metrics, deprecating functions, and maintaining the package.
401|61
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npx skills add tidymodels/yardstick --all -g -y
Available:

Gives your AI agent the exact R commands, coding conventions, testing rules, and documentation standards to follow when developing and maintaining this package.

All Skills in This Repository (3)

Pure Emerald Level Indicators
📦 In Repo
tidymodelstidymodels

detect-metrics

List all metric functions available in the yardstick R package.

Official
Basic
📦 In Repo
tidymodelstidymodels

tidy-deprecate-function

Deprecate R functions and arguments with lifecycle warnings and documentation updates.

Official
Intermediate
📦 In Repo
tidymodelstidymodels

create-skill

Guide creation of Claude Code skills with YAML frontmatter and markdown structure.

Official
Intermediate

Frequently Asked Questions

FAQPage Schema
How to install yardstick?▼

Run `npx skills add tidymodels/yardstick --all -g -y` in your terminal to install all skills in this suite globally.

What is the yardstick R package used for?▼

Yardstick measures how well machine learning models perform using tidy data principles. It computes metrics like accuracy, ROC AUC, precision, and recall for classification, regression, and survival models.

How to calculate metrics across multiple resamples?▼

Group your data frame by resample with dplyr's group_by(), then call any metric like roc_auc(). Yardstick calculates the metric for every resample at once and returns a tidy data frame.

Does yardstick support multiclass classification metrics?▼

Yes. Every classification metric has multiclass extensions with macro, micro, and weighted averaging options, plus the Hand-Till method for multiclass ROC AUC.

Can I use these skills without deep R package experience?▼

Yes. The included skills guide your agent through detecting metrics, deprecating functions, and following package conventions step by step.

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